> ## Documentation Index
> Fetch the complete documentation index at: https://api-docs.ollang.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Deepgram Aura Text-to-Speech

> Deepgram Aura-2 text-to-speech — 88 voices across 7 languages with deep English and Spanish accent coverage.

**Deepgram Aura** is Deepgram's text-to-speech line, engineered for low-latency, high-throughput voice. Aura-2 is the current generation, aimed at enterprise applications — customer service, healthcare, sales, interviews, and IVR — where clarity and consistency matter more than expressive range.

Its voice catalog is unusual in shape: **deep rather than wide**. Seven languages, but more voices inside English and Spanish than any other engine in this catalog offers.

<Info>
  **Available in Ollang Workflows** as `deepgram` — AI Dubbing. See the [Text-to-Speech catalog](/providers/text-to-speech). Deepgram's speech recognition side is documented separately under [Deepgram](/apis/stt-apis/deepgram).
</Info>

## Voices and languages

Aura-2 provides **88 voices across 7 languages**:

| Language | Aura-2 voices | Accents                                        |
| -------- | ------------- | ---------------------------------------------- |
| English  | 38            | American, British, Australian, Irish, Filipino |
| Spanish  | 17            | Mexican, Peninsular, Colombian, Latin American |
| Italian  | 10            | —                                              |
| Dutch    | 9             | —                                              |
| German   | 7             | —                                              |
| Japanese | 5             | —                                              |
| French   | 2             | —                                              |

Voices are addressed as `[model]-[voice]-[language]`, for example `aura-2-thalia-en`. Aura 1 remains available as a legacy generation with 12 English voices.

## Key capabilities

* **Accent depth within languages** — five English accents and four Spanish accents mean regional variants can be cast properly rather than approximated. For an English-to-Spanish program targeting several Latin American markets, this is a genuine advantage over engines with one Spanish voice.
* **Range of speaking styles** across genders, age groups, and delivery — warm conversational, confident professional, and characterful storytelling.
* **Low latency at scale** — the architecture is built for real-time voice agents, which translates into fast batch throughput for dubbing.
* **Efficient batch throughput**, which suits large instructional and corporate libraries.

## Where it fits in a workflow

Deepgram is a good fit when your target languages sit inside its seven and the content is instructional, corporate, or informational. For an English-to-Spanish training library the accent depth is a real product advantage rather than a rounding difference.

## Trade-offs

* **Seven languages is the narrowest coverage in the catalog.** Confirm your language pair before selecting it — this is the most common reason a Deepgram Workflow has to be reconfigured. French in particular has only two voices.
* **Built for voice agents, not narration.** On emotionally driven or performed content it reads accurately but does not act.
* No voice cloning.

## Reference

* [Text-to-Speech provider catalog](/providers/text-to-speech)
* [Deepgram voices and languages](https://developers.deepgram.com/docs/tts-models)